De-aliasing
8 papers with code • 0 benchmarks • 0 datasets
De-aliasing is the problem of recovering the original high-frequency information that has been aliased during the acquisition of an image.
Benchmarks
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Latest papers
When Semantic Segmentation Meets Frequency Aliasing
While positively correlated with the proposed aliasing score, three types of hard pixels exhibit different patterns.
Adaptive Diffusion Priors for Accelerated MRI Reconstruction
icon-lab/AdaDiff
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A two-phase reconstruction is executed following training: a rapid-diffusion phase that produces an initial reconstruction with the trained prior, and an adaptation phase that further refines the result by updating the prior to minimize data-consistency loss.
A Plug-and-Play Approach to Multiparametric Quantitative MRI: Image Reconstruction using Pre-Trained Deep Denoisers
ketanfatania/qmri-pnp-recon-poc
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This paper proposes an iterative deep learning plug-and-play reconstruction approach to MRF which is adaptive to the forward acquisition process.
Complementary Time-Frequency Domain Networks for Dynamic Parallel MR Image Reconstruction
cq615/kt-Dynamic-MRI-Reconstruction
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The iterative model is embedded into a deep recurrent neural network which learns to recover the image via exploiting spatio-temporal redundancies in complementary domains.
Compressive MR Fingerprinting reconstruction with Neural Proximal Gradient iterations
Consistency of the predictions with respect to the physical forward model is pivotal for reliably solving inverse problems.
HighRes-net: Recursive Fusion for Multi-Frame Super-Resolution of Satellite Imagery
Multi-frame Super-Resolution (MFSR) offers a more grounded approach to the ill-posed problem, by conditioning on multiple low-resolution views.
HighRes-net: Multi-Frame Super-Resolution by Recursive Fusion
ElementAI/HighRes-net
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ICLR 2020
Multi-frame Super-Resolution (MFSR) offers a more grounded approach to the ill-posed problem, by conditioning on multiple low-resolution views.
Can learning from natural image denoising be used for seismic data interpolation?
We propose a convolutional neural network (CNN) denoising based method for seismic data interpolation.
